Publicação: Classification of masses in mammographic image using wavelet domain features and polynomial classifier
dc.contributor.author | Nascimento, Marcelo Zanchetta Do | |
dc.contributor.author | Martins, Alessandro Santana | |
dc.contributor.author | Neves, Leandro Alves [UNESP] | |
dc.contributor.author | Ramos, Rodrigo Pereira | |
dc.contributor.author | Flores, Edna Lúcia | |
dc.contributor.author | Carrijo, Gilberto Arantes | |
dc.contributor.institution | Universidade Federal de Uberlândia (UFU) | |
dc.contributor.institution | Federal Institute of Triangulo Mineiro (IFTM) | |
dc.contributor.institution | Universidade Estadual Paulista (Unesp) | |
dc.contributor.institution | Universidade Federal do Vale do São Francisco (UNIVASF) | |
dc.date.accessioned | 2014-05-27T11:29:46Z | |
dc.date.available | 2014-05-27T11:29:46Z | |
dc.date.issued | 2013-06-21 | |
dc.description.abstract | Breast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98 ± 0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms. © 2013 Elsevier Ltd. All rights reserved. | en |
dc.description.affiliation | Faculty of Computation (FACOM) Federal University of Uberlândia (UFU), Uberlândia, MG | |
dc.description.affiliation | Federal Institute of Triangulo Mineiro (IFTM), Ituiutaba, MG | |
dc.description.affiliation | Department of Electrical Engineering Federal University of Uberlândia (UFU), Uberlândia, MG | |
dc.description.affiliation | Department of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), São José do Rio Preto, SP | |
dc.description.affiliation | College of Electrical Engineering (CENEL) Federal University of Vale Do São Francisco (UNIVASF), Juazeiro, BA | |
dc.description.affiliationUnesp | Department of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), São José do Rio Preto, SP | |
dc.format.extent | 6213-6221 | |
dc.identifier | http://dx.doi.org/10.1016/j.eswa.2013.04.036 | |
dc.identifier.citation | Expert Systems with Applications, v. 40, n. 15, p. 6213-6221, 2013. | |
dc.identifier.doi | 10.1016/j.eswa.2013.04.036 | |
dc.identifier.issn | 0957-4174 | |
dc.identifier.lattes | 2139053814879312 | |
dc.identifier.scopus | 2-s2.0-84879043579 | |
dc.identifier.uri | http://hdl.handle.net/11449/75693 | |
dc.identifier.wos | WOS:000322051600042 | |
dc.language.iso | eng | |
dc.relation.ispartof | Expert Systems with Applications | |
dc.relation.ispartofjcr | 3.768 | |
dc.relation.ispartofsjr | 1,271 | |
dc.rights.accessRights | Acesso restrito | |
dc.source | Scopus | |
dc.subject | Mammography | |
dc.subject | Polynomial classifier | |
dc.subject | Texture analysis | |
dc.subject | Wavelet CAD | |
dc.subject | Area under the ROC curve | |
dc.subject | Artificial intelligence algorithms | |
dc.subject | Classification algorithm | |
dc.subject | Digitized mammograms | |
dc.subject | Receiver operating characteristics curves (ROC) | |
dc.subject | Wavelet domain features | |
dc.subject | Algorithms | |
dc.subject | Artificial intelligence | |
dc.subject | Computer aided diagnosis | |
dc.subject | Decision trees | |
dc.subject | Discrete wavelet transforms | |
dc.subject | Diseases | |
dc.subject | Multiresolution analysis | |
dc.subject | Orthogonal functions | |
dc.subject | Textures | |
dc.subject | X ray screens | |
dc.subject | Polynomials | |
dc.title | Classification of masses in mammographic image using wavelet domain features and polynomial classifier | en |
dc.type | Artigo | |
dcterms.license | http://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy | |
dspace.entity.type | Publication | |
unesp.author.lattes | 2139053814879312 | |
unesp.author.orcid | 0000-0002-9243-3887[4] | |
unesp.author.orcid | 0000-0001-8580-7054[3] | |
unesp.campus | Universidade Estadual Paulista (UNESP), Instituto de Biociências Letras e Ciências Exatas, São José do Rio Preto | pt |
unesp.department | Ciências da Computação e Estatística - IBILCE | pt |